Multiplexing trajectories of moving objects

Patroumpas, K; Toumbas, K; Sellis, T

dc.contributor.author	Patroumpas, K	en
dc.contributor.author	Toumbas, K	en
dc.contributor.author	Sellis, T	en
dc.date.accessioned	2014-03-01T02:53:56Z
dc.date.available	2014-03-01T02:53:56Z
dc.date.issued	2012	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36490
dc.subject.other	Encoding schemes	en
dc.subject.other	Historical trace	en
dc.subject.other	Maintenance cost	en
dc.subject.other	Moving objects	en
dc.subject.other	Multiplexing schemes	en
dc.subject.other	On-line detection	en
dc.subject.other	Real-time information	en
dc.subject.other	Multiplexing	en
dc.subject.other	Trajectories	en
dc.title	Multiplexing trajectories of moving objects	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-31235-9_42	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-31235-9_42	en
heal.publicationDate	2012	en
heal.abstract	Continuously tracking mobility of humans, vehicles or merchandise not only provides streaming, real-time information about their current whereabouts, but can also progressively assemble historical traces, i.e., their evolving trajectories. In this paper, we outline a framework for online detection of groups of moving objects with approximately similar routes over the recent past. Further, we propose an encoding scheme for synthesizing an indicative trajectory that collectively represents movement features pertaining to objects in the same group. Preliminary experimentation with this multiplexing scheme shows encouraging results in terms of both maintenance cost and compression accuracy. © 2012 Springer-Verlag.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-31235-9_42	en
dc.identifier.volume	7338 LNCS	en
dc.identifier.spage	595	en
dc.identifier.epage	600	en